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Conditional density estimators

Source: R/density_estimator.R
density_estimator.Rd

A conditional density estimator learns \(q_\phi(\theta \mid x)\). In neuralsbi every estimator is trained in standardized space and exposes two generics:

Details

  • de_log_prob(de, theta, x) – log density of theta given x

  • de_sample(de, x, n) – draw n parameter vectors given a single x

Two estimators ship today:

  • "mdn" – a Mixture Density Network (neural network -> Gaussian mixture), the workhorse, requires the torch back end.

  • "linear_gaussian" – a closed-form conditional Gaussian baseline (least-squares mean, residual covariance). No neural network, no torch. It is exact for linear-Gaussian simulators and doubles as a fast baseline and a regression-test oracle.